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IntroductionGroundwater demand has been considerably heightened due to rapid urban growth, specifically in arid areas that rely primarily on groundwater. This study aims to utilize remote sensing and aeromagnetic data, combined with the Analytic Hierarchy Process (AHP) based GIS, to evaluate potential groundwater zones in the Sohag area, Egypt.MethodsNine thematic layers, including soil moisture, rainfall, lithology, normalized difference vegetation index (NDVI), drainage density, lineament density, slope, and land use/land cover, were developed using various remote sensing datasets. Besides the remote sensing-derived thematic layers, a geophysics-derived thematic layer represented by the RTP aeromagnetic map was included. The aeromagnetic data were analyzed and interpreted to outline the subsurface structure affecting groundwater storage and flow. Also, the aeromagnetic data analysis helps estimate the basement depth that constitutes the Nubian Aquifer’s base and identifies regions with considerable thick sedimentary deposits and significant water reserves.Results and discussionThe groundwater potentiality map was consistent with production wells in the area, and sites for drilling new wells were predicted, especially in the Nile Valley around the Tahta, El-Hamimia, and west Sohag cities. The most promising sites are clustered along the Nile Valley, and the study area’s northwestern and northeastern parts. The results indicate that the predominant magnetic structural trends are NW-SE, NE-SW, N-S, and E-W, which contribute to the formation of a series of subsurface horsts (H) and grabens (G). Three main basins (A, B, and C) were identified as the most profound areas. These basins represent the most promising areas for groundwater accumulation, making them attractive for future hydrogeological exploration. This integrated approach strongly offers a powerful and effective tool to assist in developing an appropriate plan to manage groundwater in arid regions.

A prospective study was conducted from September 2022 to March 2023 to examine the occurrence and contamination rates of different parasitic stages on fresh edible fruits and vegetables in local markets of Erbil city. Eight different types of fruits and vegetables were selected from eight markets, and 144 samples were analyzed for parasites using direct sedimentation, indirect floatation (ZnSO4), Kinyoun, and trichrome staining methods. The highest total frequencies of parasites were detected using the direct sedimentation method (18.75%), followed by the indirect floatation method (6.25%), Kinyoun technique (5.55%), and modified trichrome staining (4.16%). A. lumbricoides (Ova) was detected with 4.16% using direct sedimentation, while no positive detections were observed with other techniques. Kinyoun technique identified Cryptosporidium spp. oocysts. For E. histolytica 1.3% were positive using the direct sedimentation method, while the other three methods showed no positive detections. Similar results were observed for G. lamblia where only the modified trichrome staining technique detected 0.69% with trophozoites and 1.3% with cysts. Additionally, the direct sedimentation method revealed the presence of nematodes at 2.77%, but no positive detections were made with other methods. Concerning Taeniid eggs, the indirect floatation identified 0.69% with ova, while other methods showed no positive detections. Lastly, the direct sedimentation method yielded 6.25% with unidentified flagellate protozoa, but no positive detections were found using the indirect floatation method, Kinyoun technique, or modified trichrome staining technique. The study concluded that Kinyoun and trichrome techniques are the gold standard for detecting different parasite stages.

Background: Cell adhesion molecules are protein entities that are located on the cell surface. The vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1) expression is related to type 2 diabetes mellitus (T2DM) with dyslipidemia. Objectives: To determine the levels of VCAM-1 and ICAM-1 in T2DM patients with dyslipidemia and to explore the relationship between VCAM-1 and ICAM-1 and the development of dyslipidemia in T2DM patients. Patients and methods: The study included 150 individuals with an age range of (35-55) years. Patients with diabetes for more than 5 years were excluded. Fifty healthy individuals constituted Group 1 (G1), fifty patients with T2DM constituted Group 2 (G2), and fifty T2DM patients with dyslipidemia constituted Group 3 (G3). Whole blood samples were drawn to measure HbA1c based on fluorescence immunoassay technology. The serum was separated to measure fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), and high-density lipoproteins (HDL) by manual methods, while VCAM-1, and ICAM-1 were determined using the ELISA test. The study was conducted between November 2022 and April 2023 at the National Center for Diabetes Treatment and Research, Baghdad, Iraq. Results: Significantly higher levels of FSG and HbA1c were detected in G2 and G3 compared to G1, but non-significantly so when G3 was compared to G2. Significant higher levels of TG and TC levels were detected for G3 when compared to G1 and G2, but non-significantly so when G2 was compared to G1. HDL levels were significantly lower in G3 compared to G2 and G1, but non-significantly so when G2 was compared to G1. VCAM-1, and ICAM-1 were significantly higher in G2 compared to G1, and VCAM-1 level was significantly higher in G3 compared to G2. Non-significant differences in ICAM-1 levels were found between G3 and G2. Conclusion: VCAM-1 and ICAM-1 are potentially significant factors in the development of dyslipidemia in diabetes patients. They might serve as biomarkers to accurately predict the progression of cardiovascular disease.

Umamah bint Khalid,1 Muddasar Naeem,2 Fabrizio Stasolla,2 Madiha Haider Syed,1,3 Musarat Abbas,1 Antonio Coronato2 1Department of Electronics, Quaid-I-Azam University, Islamabad, 44000, Pakistan; 2Research Center on ICT Technologies for Healthcare and Wellbeing, Università Telematica “Giustino Fortunato”, Benevento, 82100, Italy; 3Institute of Information Technology, Quaid-i-Azam University, Islamabad, 44000, PakistanCorrespondence: Muddasar Naeem, Email m.naeem@unifortunato.euAbstract: Rehabilitation is an important and necessary part of local and global healthcare services along with treatment and palliative care, prevention of disease, and promotion of good health. The rehabilitation process helps older and young adults even children to become as independent as possible in activities of daily life and enables participation in useful living activities, recreation, work, and education. The technology of Artificial Intelligence (AI) has evolved significantly in recent years. Many activities related to rehabilitation have been getting benefits from using AI techniques. The objective of this review study is to explore the advantages of AI for rehabilitation and how AI is impacting the rehabilitation process. This study aims at the most critical aspects of the rehabilitation process that could potentially take advantage of AI techniques including personalized rehabilitation apps, rehabilitation through assistance, rehabilitation for neurological disorders, rehabilitation for developmental disorders, virtual reality rehabilitation, rehabilitation of neurodegenerative diseases and Telerehabilitation of Cardiovascular. We presented a survey on the newest empirical studies available in the literature including the AI-based technology helpful in the Rehabilitation process. The novelty feature included but was not limited to an overview of the technological solutions useful in rehabilitation. Seven different categories were identified. Illustrative examples of practical applications were detailed. Implications of the findings for both research and practice were critically discussed. Most of the AI applications in these rehabilitation types are in their infancy and continue to grow while exploring new opportunities. Therefore, we investigate the role of AI technology in rehabilitation processes. In addition, we do statistical analysis of the selected studies to highlight the significance of this review work. In the end, we also present a discussion on some challenges, and future research directions.Keywords: artificial intelligence, machine learning, rehabilitation, healthcare, cognitive skills, cognitive rehabilitation

Abstract We investigate the charged Vaidya spacetime with conformal symmetry by classifying the horizons and finding its connection to Hawking temperature. We find a conformal Killing vector whose existence requires the mass and electric charge functions to be proportional, as well as linear in time. Solving the Killing equations for the conformally transformed metric from the linear charged Vaidya metric yields the required form of the conformal factor. From the vanishing of the norm of the conformal Killing vector, we find three conformal Killing horizons which, under the transformation, are mapped to the Killing horizons of the associated static spacetime, if the spherical symmetry is maintained. We find that the conformal factor is not uniquely determined, but can take any function of the ratio of the radial coordinate to the dynamical mass. As an example, we illustrate a static spacetime with our choice of the conformal factor and explicitly show that the surface gravity of the conformal Killing horizons, which is conformally invariant, yield the expected Hawking temperature in the static spacetime. This static black hole spacetime contains a cosmological horizon, but it is not asymptotically de Sitter. We also investigate the case when the mass parameter is equal to the constant electric charge. While in this case the standard pair of horizons, the loci of the time component of the metric, degenerate, the conformal Killing horizons do not degenerate. This therefore leads to a non-zero Hawking temperature in the associated static spacetime.

The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data, it may falter in achieving optimality due to the nonlinear nature of energy data. Conversely, meta-heuristic algorithms offer optimization capabilities but suffer from computational burdens, especially with high-dimensional data. This paper provides a comprehensive review spanning 2018 to 2023, examining the integration of meta-heuristic algorithms within deep learning frameworks for energy applications. We analyze state-of-the-art techniques, innovations, and recent advancements, identifying open research challenges. Additionally, we propose a novel framework that seamlessly merges meta-heuristic algorithms into deep learning paradigms, aiming to enhance performance and efficiency in addressing energy-related problems. The contributions of the paper include:1. Overview of recent advancements in MHs, DL, and integration.2. Coverage of trends from 2018 to 2023.3. Introduction of Alpha metric for performance evaluation.4. Innovative framework harmonizing MHs with DL for energy problems.

Face recognition has become indispensable in our daily lives as a quick and painless technique of confirming our identities since in the era of wearing face masks the traditional face recognition system may not effectively recognize the concerned person as an important part of the face (mouth, nose, and chin) which makes a substantial contribution to the face recognition process are occluded and partially hidden. The objective of our research is to tackle the challenges of the partially occluded face with a mask by training our custom dataset using those powerful pre-trained deep learning models like YOLO and Mask R-CNN which have not been used before for this purpose and to compare which one is outperforming the better results. To this end, models like (YOLOv5, YOLOv7, YOLOv8, and Mask R-CNN) have been employed and trained on the created dataset to check the accuracy and robustness of the occluded face recognition process. In addition, an online dataset such as (mfr2) which contains celebrities, and politicians masked and unmasked faces after expansion with more images has been used. The experimental findings demonstrate that the proposed algorithms give an accurate result, we achieve an accuracy of 97.5% using YOLOv8s, an accuracy of 89.7% using YOLOv7, and an accuracy of 89% using YOLOv5x, while an accuracy of 94.5% using Mask R-CNN. The study concludes that YOLOv8s outperforms the other models in masked face recognition.

Cancer is one of the danger diseases in our life, especially lung cancer is one of the most effected organs by cancer and causes death. The early detection of the tumor is very important issue for staging the cancer phases, usually the shape and size of the tumor are considered for classify the cancer type, calculation size of the tumor area and detecting it in accurate way will help to save patient life, this paper uses Mask-RCNN to analyze and detect malignant and benign tumor with real dataset of CT scan lung cancer images in (Kurdistan Region of Iraq) KRI, also develop calculating area size of tumor in cm2. After training and testing the system accuracy 96.59%, sensitivity 95%, specificity 95% and F_1 score 99.65% have been achieved. The study concludes that Mask-RCNN is a very good model for diagnoses cancer tumors and can help radiologists to detect and stage the cancer.

Traffic analysis is one of the crucial tasks of intelligent transport system that utilizes deep learning for range of purposes. Many tasks, such as vehicle recognition, vehicle counting, traffic violation monitoring, vehicle speed monitoring, vehicle density and so on, can be accomplished by using cameras installed in strategic locations along roads. In this paper powerful deep learning techniques such as (Yolov5, Mask R-CNN, SSD) and state-of-the-art object tracking algorithm known as DeepSORT was used to perform real time vehicle detection and counting in a video. A new highway vehicle detection dataset with overall of 32,265, instances of four vehicle classes named: bus, car, motorbike, truck was created in this paper and utilized for training vehicle detection and counting system. Result shows that average counting accuracy by using Yolov5 combined DeepSORT reaches to 95% while reaches to 91% by using Mask R-CNN combined DeepSORT and 84% by using SSD combined DeepSORT in hard environment. From the experimental work, counting accuracy by using Yolov5 outperforms other two deep learning techniques.

Hundreds of infrastructure investment projects are inserted into the Iraqi government budget annually. Many of these projects suffer from suspension, delay in completion, corruption, or other negative issues known in Iraqi construction projects. This may harm the country's economic recovery due to allocating funds to useless projects from one or more feasibility studies' components. Therefore, the best solution is to pay attention to the feasibility study reports. This study highlights the importance of feasibility studies reports for investment projects and their effective role in evaluating project ideas to ensure an equitable distribution of a country's financial resources to the most feasible investment projects. This importance was highlighted through a practical case study for a solid waste treatment project. The results of the case study showed that feasibility studies provide very important information on which investors and governments can rely to make the appropriate decisions for investment projects. The study also addressed the risks that investors can be exposed to in Iraq and how to include them in the economic analysis of feasibility studies.

The construction industry plays a crucial role in the countries' economy, especially in the developed country. This point encourages the concerned institution to use new techniques and integrate many techniques and methods to maximize the benefits. The main objective of this research is to evaluate the use of risk management, value management, and building information modeling in the Iraqi construction industry. The evaluation process aims at two objectives. The direct objective was to evaluate the knowledge in risk management (RM), value management (VM), and building information modeling (BIM). The indirect objective was to support the participants with information related to the main items mentioned. The questionnaire survey was used as a data collection method. The participants in the survey were engineers from the Iraqi construction sector. The sample size was 121 engineers from 34 institutions with multidisciplinary backgrounds. The results showed that the engineers need to develop the training system and merge many methods and techniques in their institution to maximize the benefits using an integration model capable of dealing with the variable environment in construction, especially in developing countries like Iraq.

Background: Some anthropometric, laboratory, and genetic variations, such as patatin-like phospholipase domain-containing protein 3 (PNPLA3) genetic variants, have been associated with nonalcoholic fatty liver disease (NAFLD). Liver biopsy is the most accurate NAFLD diagnostic method, but it is invasive; hence, noninvasive diagnostics are required for the early diagnosis and assessment of NAFLD. Patient and methods: This prospective case-control study included 107 NAFLD patients and 107 healthy controls. All individuals underwent anthropometric measurements, abdominal ultrasonography, laboratory tests, and evaluation for PNPLA3 polymorphisms. Results: Patients with NAFLD had higher levels of C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6) than healthy individuals (p = 0.03, p

Endogenous metabolism is primarily responsible for losses in sucrose content and processing quality in postharvest sugarbeet roots. The genes responsible for this metabolism and the transcriptional changes that regulate it, however, are largely unknown. To identify genes and metabolic pathways that participate in postharvest sugarbeet root metabolism and the transcriptional changes that contribute to their regulation, transcriptomic and metabolomic profiles were generated for sugarbeet roots at harvest and after 12, 40 and 120 d storage at 5 and 12°C and gene expression and metabolite concentration changes related to storage duration or temperature were identified. During storage, 8656 genes, or 34% of all expressed genes, and 225 metabolites, equivalent to 59% of detected metabolites, were altered in expression or concentration, indicating extensive transcriptional and metabolic changes in stored roots. These genes and metabolites contributed to a wide range of cellular and molecular functions, with carbohydrate metabolism being the function to which the greatest number of genes and metabolites classified. Because respiration has a central role in postharvest metabolism and is largely responsible for sucrose loss in sugarbeet roots, genes and metabolites involved in and correlated to respiration were identified. Seventy-five genes participating in respiration were differentially expressed during storage, including two bidirectional sugar transporter SWEET17 genes that highly correlated with respiration rate. Weighted gene co-expression network analysis identified 1896 additional genes that positively correlated with respiration rate and predicted a pyruvate kinase gene to be a central regulator or biomarker for respiration rate. Overall, these results reveal the extensive and diverse physiological and metabolic changes that occur in stored sugarbeet roots and identify genes with potential roles as regulators or biomarkers for respiratory sucrose loss.

Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms' advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm).

Objective(s): To evaluate the gastroprotective potential of zafirlukast against indomethacin-induced gastric ulcers in rats.Materials and Methods: Thirty-two male Wistar rats were included in this study and randomly divided into 4 equal groups (n=8); control (normal) group, indomethacin group, Ranitidine group, and Zafirlukast group. Indomethacin was given as a single oral dose of (20 mg/kg) for the induction of ulcers. Both ranitidine (50 mg/kg) and zafirlukast (20 mg/ kg) were given orally for seven days after inducing the ulcer. All animals were sacrificed by an overdose of anesthesia at the end of the experimental period and their gastric tissues have been collected for histopathological and biological assay. Levels of prostaglandin E2 (PGE2), thiobarbituric acid reactive substances (TBARS), and interleukin 1β (IL-1β ) were measured as well as a histopathological study to evaluate the effect of zafirlukast on gastric tissues.Results: Significant abnormalities were found in both the histological and biochemical parameters of the indomethacin group reflecting the changes seen with gastric ulcers. Significant improvement was found in the Zafirlukast group as reflected by the morphological improvement seen in the gastric tissues. An effect that was associated with an increase in the PGE2 levels along with reductions in IL-1β expression and TBARS concentrations.Conclusion: As per the results of this study, zafirlukast shows promising gastroprotective properties possibly through enhancement of PGE2 levels as well as having anti-inflammatory and anti-oxidant properties.

المدن المائية مصطلح يطلق على التجمعات العمرانية المتداخلة مع المسطحات المائية، أشهرها مدينتي البندقية بإيطاليا وامستردام بهولندا التي تتسم بالقنوات المائية والكباري. ويوجد مدن حديثة مثل جزيرة النخلة الصناعية بدبي، والمنتجعات السياحية بالمالديف المشتهرة بالبناء على دعامات فوق الماء. وتنتشر في مصر التجمعات المائية مثل بعض قري الصيادين في البحيرات الشمالية ومنطقة المكس بالإسكندرية ومدينة الجونة بالغردقة. ويساعد دمج القنوات والمساحات المائية في المدن علي زيادة النشاط الاقتصادي والسياحة والنقل بتكلفة وتلوث أقل، وتوفر إطلالات جمالية وراحة حرارية، ويمكن ان تساعد على مواجهة مخاطرارتفاع مستوي سطح البحر.وبالرغم من انتشار المدن المائية وتعدد فوائدها وأنواعها، مازال لا يوجد تعريف أو تصنيف متفق عليه، فنجد المصطلح يطلق على أي تجمع متداخل مع الماء، بالرغم من وجود اختلاف في السمات والخصائص بين تجمعاتها. وهنا يأتي دور البحث في تحديد وتصنيف الأنماط الرئيسية للمدن المائية، واستنتاج خصائص وسمات كل نوع، لكي يساهم في فهم المدن المائية القائمة وتطويرها والحفاظ عليها أو تخطيط وتصميم مدن مائية جديدة على أساس علمي واضح. ولقد قام البحث من خلال منهج وصفي تحليلي تحديد هيكل عام لتصنيف أنواع المدن المائية من خلال تحليل مقارن لخمسة وثلاثين حالة دراسية حول العالم. ولقد تم تطبيق هذا الهيكل على الحالات الدراسية من خلال منهج تطبيقي تحليلي لتحديد للأنماط الرئيسية للمدن المائية. وخلص البحث الي أن التصنيف الرئيسي لأنماط المدن المائية هو الذي ينبني على طرق التخطيط والتشكيل العمراني للمدينة وهي (مدن القنوات، مدن الجزر الاصطناعية، المدن العائمة، مدن القوائم). كما تم استنتاج السمات الرئيسية لكل لنوع من خلال دراسة مقارنة بين الأنماط الرئيسية للمدن المائية والهيكل العام للتصنيف. Water cities are urban communities that overlap water bodies, most notably cities such as Venice in Italy and Amsterdam in the Netherlands, which are characterized by aqueducts and bridges. There are modern cities, such as Dubai's Palm Jumeirah Artificial Island, as well as tourist attractions in the Maldives famous for their pillars built on water. Water communities are widely distributed in Egypt, such as some fishermen's villages in the northern lakes, Al Max area in Alexandria, and the city of El Gouna in Hurghada. The integration of aqueducts and water bodies in cities helps increase economic activity, tourism and transportation at lower cost and pollution. It provides scenic views and thermal comfort and helps address the risks of rising sea levels. Despite the wide distribution of water cities and their variety of strengths and types, no agreed definition or classification has yet been reached. The term refers to any community overlapped with water, although there were differences in features and characteristics between its communities. The research identifies and classifies the main types of water cities, and deduces the features and characteristics of each type, in order to help understand, develop and protect the existing water cities or plan and design new water cities on a clear scientific basis. Using a descriptive and analytical approach, the research identifies a general structure of water city typology through a comparative analysis of thirty-five case studies around the world. It is done by applying the analytical methods to identify the main types of water cities. The research concluded that the main classification of water city models is mainly based on urban planning and urban formation methods, namely (canal city, artificial island city, floating city, stilt city). The main characteristics of each type are deduced through the comparative study of the main types of water cities and the overall structure of the classification.

Abstract Patient-level characteristics associated with the prevalence and severity of financial anxiety have yet to be described. We conducted a cross-sectional analysis of survey data assessing financial anxiety in patients with chronic medical conditions in December 2020. 1771 patients (42.6% response rate) participated in the survey. Younger age (19–35 age compared to ≥ 75 age) (β, 5.86; 95% CI 2.10–9.63), male sex (β, − 1.9; 95% CI − 3.1 to − 0.73), Hispanic/Latino race/ethnicity (compared with White patients) (β, 2.55; 95% CI 0.39–4.71), household size ≥ 4 (compare with single household) (β, 4.54; 95% CI 2.44–6.64), household income of ≥ $96,000-$119,999 (compared with ≤ $23,999) (β, − 3.2; 95% CI − 6.3 to 0.04), single marital status (compared with married) (β, 2.18; 95% CI 0.65–3.71), unemployment (β, 2.07; 95% CI 0.39–3.74), high-school education (compared with advanced degrees) (β, 3.10; 95% CI 1.32–4.89), lack of insurance coverage (compared with private insurance) (β, 6.05; 95% CI 2.66–9.45), more comorbidities (≥ 3 comorbidities compared to none) (β, 2.95; 95% CI 1.00–4.90) were all independently associated with financial anxiety. Patients who are young, female, unmarried, and representing vulnerable sub-populations are at elevated risk for financial anxiety.

The unpredictable and huge data generation nowadays by smart devices from IoT and mobile Crowd Sensing applications like (Sensors, smartphones, Wi-Fi routers) need processing power and storage. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important of these limitations are Resource Allocation and Task Scheduling. The resource allocation process is a mechanism that ensures allocation virtual machine when there are multiple applications that require various resources such as CPU and I/O memory. Whereas scheduling is the process of determining the sequence in which these tasks come and depart the resources in order to maximize efficiency. In this paper we tried to highlight the most relevant difficulties that cloud computing is now facing. We presented a comprehensive review of resource allocation and scheduling techniques to overcome these limitations. Finally, the previous techniques and strategies for allocation and scheduling have been compared in a table with their drawbacks.

Hafiz Muhammad Zubair,1 Muhammad Arslan Khan,2 Faisal Gulzar,1 Musaed Alkholief,3 Abdul Malik,3 Suhail Akhtar,4 Ali Sharif,5 Muhammad Furqan Akhtar,6 Muhammad Abbas7 1Faculty of Pharmacy, The University of Lahore, Lahore, Pakistan; 2Department of Pharmacy Services, The University of Lahore Teaching Hospital, Lahore, Pakistan; 3Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia; 4Department of Biochemistry, A.T. Still University of Health Sciences, Kirksville, MO, USA; 5Department of Pharmacology, Institute of Pharmacy, Lahore College for Women University, Lahore, Pakistan; 6Riphah Institute of Pharmaceutical Sciences, Riphah International University, Lahore Campus, Lahore, Pakistan; 7Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, PakistanCorrespondence: Hafiz Muhammad Zubair, Faculty of Pharmacy, The University of Lahore, Lahore, Pakistan, Email dr.zubair83@gmail.comPurpose: This study aimed to explore patients’ experiences of palliative chemotherapy for non-small cell lung cancer (NSCLC), how patients adapt to their new and challenging life after chemotherapy, their beliefs, and their quality of life.Patients and Methods: The study used an exploratory descriptive qualitative approach that was designed to explore the experiences and side effects of NSCLC patients on chemotherapy in Pakistan. The study was designed to obtain a deeper understanding of 22 NSCLC patients’ experiences, using a face-to-face approach and interviews were conducted. Patients who have completed chemotherapy agreed to participate in semi-structured interviews.Results: The data were arranged into five themes: hospital facilities and environment, patient’s beliefs in alternative treatments, presenting a positive/negative face, life is for living, and health insurance coverage. The major complaints related to bad experiences of chemotherapy-induced side effects, but these patients still managed to complete the full course of their respective chemotherapy. Additionally, the current study revealed the real experience of patients with NSCLC which had been less studied. The patient’s experience was summarized into four themes and several subthemes.Conclusion: This study aid healthcare providers when deciding on treatment options that will improve shared decision-making between clinicians and treatment outcomes.Keywords: chemotherapy, NSCLC, qualitative research, semi-structured interview

The construction process is fraught with risks and uncertainties, which grow in proportion to the size and complexity of the project. The project is considered successful if it is completed within the specified time and cost and with the required quality standards. The time and cost overrun of building projects and highways is a critical issue because of the great risks that result from it, which affect the project completion process and sometimes put it to a halt. In this paper, an attempt was made to predict the final cost rate and final time rate of such projects before starting work, using the technique of reverse elimination regression analysis. The study covers a group of highway projects in Diyala Governorate, where the records of (25) projects that were completed during (2012-2021) were used to develop and validate the regression model. Based on the experts' convictions, there are nine factors that are considered the most influential on the final cost and final time. Hence they are used as input parameters for the form. These factors are; bid cost, bid duration, change order cost, change order duration, final cost, and final duration, no. of change order, start time and final time. It was found that the developed regression model has the ability to predict the final cost rate (FCR) and final duration rate (FDR) for highway projects as outputs with very high goodness of fit and coefficient determination ( ) of (98%) and (96%) for FCR and FDR respective.

Construction projects are of great importance because of their large financial allocations in the federal budgets of countries. This study used risk management and value management as an integrative model with Building Information Modeling (BIM). Risk management techniques were used to identify risks and quantify their impact on cost, and value management was used to identify effective mitigation measures for each risk factor. This study developed software to support BIM and risk management, including measures to assess risks and their impact on the cost estimation process, to be part of the information flowing to the project parties. The total number of risks was 52 with multiple mitigation measures for each determined using brainstorming. The Fuzzy Analytical Hierarchal Process (FAHP) was used to determine the relative importance of risk and the cost range of the selected items.

This essay aims to highlight the most important issues and difficulties facing implementing large projects that follow the turn-key method, considered one of the types of contractual methods in Iraq, especially for large and complex projects requiring speedy completion. The projects implemented in this way face delays and delays in completion, which led to the lack of benefit from the projects for which they were implemented, especially those affecting the lives of citizens within the health sector. The case study dealt with the construction of hospitals with multi-bed capacities within multiple governorates in Iraq, With large financial allocations within the federal budget of the Government of Iraq over several years. After conducting a descriptive statistical analysis of the data obtained through a field survey and interviews with individuals working in the construction sector with experience in their field. The most important 10 factors were identified from a list of 51 delay factors (distributed according to the questionnaire forms) with a high RII. These factors were related to the delay in the payment of the contractor's dues, the delay in processing materials, the issuance of new instructions and regulations, the delay in obtaining official approvals from the relevant authorities, Errors in designs or contracting documents, lack of cooperation between the contracting parties, and multiple spare orders for reasons belonging to the employer. The article also included the most important suggestions that contribute to reducing the delay in completing projects.

Artificial levees have major importance in protecting human lives and infrastructure as they are essential elements of the flood protection measures. Nevertheless, the lack of the necessary information about their structure and internal composition might cause high risks. To monitor their stability, integrated surveys are needed, including geophysical and geotechnical methods. Levees along the rivers in Hungary were constructed more than 150 years ago, and they were heightened several times; therefore, investigations are required to assure their performance in flood risk mitigation. Our investigation aimed to utilise non-invasive geophysical techniques, primarily electrical resistivity imaging, with the validation of geotechnical investigations to map and compare the compositional and structural variations of two very different levee sections along River Tisza and River Maros. Integrating the analysed drilling data with ERT profiles showed that the main composition of the investigated Tisza levee section is fine and medium silt with an average resistivity 30 Ωm, however, the investigated section of Maros levee was built of not only of fine and medium silt but also of medium and coarse sand exhibiting higher resistivity values reaching up to 2200 Ωm. Several physical parameters were measured to study the nature of constituting levee materials like moisture content, grain-size, porosity, bulk-density, saturated hydraulic conductivity, and resistivity. It was found that most of them show a connection with resistivity, but the hydraulic conductivity did not show a direct connection, however the latter could exhibit the aquitard nature of Tisza levee materials and the non-aquitard nature of Maros levee materials.

Once an image has been processed by, for example, a robot machine, for the purpose of, for example, features extraction or meaningful information retrieval, has a secure scheme been applied to preserve security and privacy of such information before sending it to another processing party? A huge number of image-related Internet of Things (IoT) applications face such an issue. But what are applied and potentially being applied image processing techniques that have contributed to enhance the security and privacy of IoT applications? There are numerous IoT applications that utilize image processing techniques in this direction. This article aims to survey and review a number of recently published papers and research studies that encompass proposed methods in which image processing techniques are applied to enhance the security, privacy, and safety of IoT applications. It also aims to help interested researchers in related fields have insights on what the role of image processing in enhancing the security of IoT applications is and what those techniques applied to enhance the security of IoT applications are. A comprehensive framework has been graphically extracted to give readers in the field of IoT security a map with the suitable image processing techniques that serve better to enhancing IoT applications in terms of security and privacy.

In the so near future, climate change caused by Carbon dioxide (CO2) emissions as a result of an increase in energy demand (ED) in the buildings sector becomes really hazard to our green environment. Both climate change and increase in ED are affected by each other. Such increase in ED would increase both climate change and global warming. Fortunately, there is, however, still plenty of time to address this issue. This increase might be significantly and effectively controlled. One of the effective solutions is to apply demand response (DR) strategies exploiting the smart grid (SG). Hence, SG contributes to help reduce both ED and CO2 emissions. Of importance is establishing SGs-adopted buildings in which DR role could act as an operating and management system aiming to reach energy sustainability. We review researches on DR strategies and technologies applied to SGs-adopted buildings. Unlike previous literature reviews considering certain types of buildings or strategies which therefore narrow the focus of review papers, this review has analyzed numerous types of buildings and a variety of technologies to expand the review’s scope. Concluded remarks, insights, challenges faced by researchers, and suggestions proposed by this review paper have been in detail discussed. Limitations, further gaps in future research on DR strategies, potential implications of DR in SG-adopted buildings have been also discussed. Towards sustainable energy in the buildings sector utilizing DR strategies, points of strength and weakness concluded from the reviewed articles have been highlighted and discussed to open trends that might potentially enhance future proposed DR strategies applied to SG-adopted buildings.

Muhammad Anwar Bashir,1 Amjad Khan,2 Sayyed Ibrahim Shah,3 Majeed Ullah,2 Fazli Khuda,4 Muhammad Abbas,3 Khang Wen Goh,5 Long Chiau Ming6 1Department of Pharmacy, Abasyn University, Peshawar, Pakistan; 2Department of Pharmacy, Kohat University of Science and Technology, Kohat, Pakistan; 3Department of Pharmacy, Abdul Wali Khan University, Mardan, Pakistan; 4Department of Pharmacy, University of Peshawar, Peshawar, Pakistan; 5Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia; 6PAP Rashidah Sa’adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei DarussalamCorrespondence: Khang Wen Goh; Long Chiau Ming, Email khangwen.goh@newinti.edu.my; long.ming@ubd.edu.bnBackground: Self-emulsifying drug-delivery systems (SEDDSs) are designed to improve the oral bioavailability of poorly water-soluble drugs. This study aimed at formulating and characterization of SEDDS-based tablets for simvastatin using castor and olive oils as solvents and Tween 60 as surfactant.Methods: The liquids were adsorbed on microcrystalline cellulose, and all developed formulations were compressed using 10.5 mm shallow concave round punches.Results: The resulting tablets were evaluated for different quality-control parameters at pre- and postcompression levels. Simvastatin showed better solubility in a mixture of oils and Tween 60 (10:1). All the developed formulations showed lower self-emulsification time (˂200 seconds) and higher cloud point (˃60°C). They were free of physical defects and had drug content within the acceptable range (98.5%– 101%). The crushing strength of all formulations was in the range of 58– 96 N, and the results of the friability test were within the range of USP (≤ 1). Disintegration time was within the official limits (NMT 15 min), and complete drug release was achieved within 30 min.Conclusion: Using commonly available excipients and machinery, SEDDS-based tablets with better dissolution profile and bioavailability can be prepared by direct compression. These S-SEDDSs could be a better alternative to conventional tablets of simvastatin.Keywords: simvastatin, self-emulsified drug-delivery system, castor oil, olive oil, Tween 60

A new ibuprofen derivative, (E)-2-(4-isobutylphenyl)-N′-(4-oxopentan-2-ylidene) propane hydrazide (IA), was synthesized, along with its metal complexes with Co, Cu, Ni, Gd, and Sm, to investigate their anti-inflammatory efficacy and COX-2 inhibition potential. Comprehensive characterization, including 1H NMR, MS, FTIR, UV–vis spectroscopy, and DFT analysis, were employed to determine the structural configurations, revealing unique motifs for Gd/Sm (capped square antiprismatic/tricapped trigonal prismatic) and Cu/Ni/Co (octahedral) complexes. Molecular docking with the COX-2 enzyme (PDB code: 5IKT) and pharmacokinetic assessments through SwissADME indicated that these compounds have superior binding energies and pharmacokinetic profiles, including BBB permeability and gastrointestinal absorption, compared to the traditional ibuprofen standalone. Their significantly lower IC50 values further suggest a higher efficacy as anti-inflammatory agents and COX-2 inhibitors. These research findings not only introduce promising ibuprofen derivatives for therapeutic applications but also set the stage for future validation and exploration of this new generation of ibuprofen compounds.

This study provides simple sensitive and selective spot tests and spectrophotometric methods for quantifying l-glutathione (GSH) as a cancer biomarker. The dipsticks are based on filter paper containing a Cu(II)-murexide complex which was prepared in phosphate buffer pH 6.0 and then incorporated inside a filter paper matrix. On addition of GSH, the spots' color progressively changed from yellow to orange through pink to violet before gradually vanishing. The indicator is released when GSH is injected because GSH displaces murexide dye from the murexide-Cu2+ complex. This enables the determination of glutathione in human serum visually using the reflectometric Red-Green-Blue (RGB) interpretation of the dipstick image data. In the spectrophotometric method, the Cu2+-murexide complex absorbance band is gradually red-shifted upon interaction with different concentrations of glutathione due to the displacement reaction.Glutathione can be determined from 0.01 to 500 μg mL−1 visually and from the evaluation of the smartphone images. The limit of detection for glutathione determination using dipsticks is 0.01 μg mL−1, which is the lowest value to date. This technique has been successfully used with positive outcomes to identify low concentrations of glutathione in samples of human serum.

Abstract The use of conventional fossil-fuel vehicles in the transportation industry contributes to climate change. The energy producing sector has actually adjusted its strategy to utilize more renewable energy to satisfy the energy demand as a result of this change in strategy. The use of electric vehicles (EVs) in the transportation network has also helped to reduce pollution from the energy industry. However, the distribution network would be placed under a lot of stress if a lot of EVs are attached to it, which might cause a number of problems and worries. However, several initiatives have been made in this direction to help find answers to these issues. The contribution of this review article is that, a number of the benefits of using the employed EVs techniques for the purpose of utilizing renewable energy sources (RESs) towards the accomplishment of a sustainable energy goal for a green environment in the so foreseeable have been highlighted; aiming to provide a larger variety of readers insights and suggestions for future research development. There have been a number of studies that have recommended various ideas and strategies for a reliable renewable energy supply. Their findings have been published in scientific journals. But the relationship between RESs and EVs in terms of energy efficiency is still needed to be further investigated to shorten such a gap in order to assess how EVs can help RESs achieve energy sustainability. Besides, the purpose of this study is to identify and assess the most recent advances and methodologies in the field. It's been possible to use numerous EV technologies to help with various problems and challenges, including the need for quick EV charging stations. However, there is a worry over the viability and applicability of EV-applied technologies that are ideally suited towards greater and more efficient usage of RESs in order to either attain or contribute to energy sustainability. In this article, we will discuss a variety of EV technologies that have been used in the past and contributed to the development of RESs. The method has applied the PRISMA 2020 strategy for inclusion and exclusion criteria. Also, the method has used specific keyword related to the topic including "electric vehicles" and "renewable energy sources". It is found that EVs can be an effective alternative for currently used fossil-fuel vehicles due to a set of reasons highlighted in this review including fast EV charging stations, efficient design for PV solar panels, and effective utilization of RESs.

Sediment textural variability and distributions of one hundred sediments samples across three major strategic Harbours, Quseir, Safaga, and Hurghada, along the Northern Red Sea have been assessed. Grain size parameters and cluster analysis have been applied to reveal indices of the marine environment, mainly the energy and hydrodynamic conditions, which controlled the mode of transportation and depositional settings. The three Harbours exhibited shallow marine environment, corresponding to siliclastic terrigenous flux, associated with carbonate sediments, which sustained biogenic fragments nearby reef and mangrove sites. Depositional settings were controlled by bottom topography, sediments sources, water depth, and closeness from shoreline. This was also coupled with the exposure degree to anthropogenic discharges that triggered sediment texture disturbance and impacted seabed habitat of Safaga Harbour. The latter is more affected by contamination than Quseir and Hurghada Harbours as its topography favored the accumulation of contaminants and acted as pollutant-traps. The C-M pattern suggested rolling as the main mode of nearshore sediments transportations across the three Harbours. Suspension transportation was recognized in Quseir Harbour with higher ratio compared with Safaga Harbour, while it was absent in Hurghada Harbour. Nevertheless, the investigated Harbours shared some common features, particularly low-energy hydrodynamic regime corresponding to their semi-isolated locations and arid climatic conditions, which ruled sediments texture and distributions.

Abstract Background Portal hypertension is considered as a major complication of liver cirrhosis. Endoscopy plays a main role in managing of gastrointestinal complications of portal hypertension. Endoscopists are at increased risk for COVID-19 infection because upper gastrointestinal (GI) endoscopy is a high-risk aerosol-generating procedure and may be a potential route for COVID-19. Objectives To compare the outcome between cirrhotic patients who underwent classic regular endoscopic variceal ligation after primary bleeding episode every 2–4 weeks, and those presented during the era of COVID-19 and their follow-up were postponed 2 months later. Methods This retrospective study included cross-matched 238 cirrhotic patients with portal hypertension presented with upper GI bleeding, 112 cirrhotic patients presented during the era of COVID19 (group A) underwent endoscopic variceal ligation, another session after 2 weeks and their subsequent follow-up was postponed 2 months later, and 126 cirrhotic patients as control (group B) underwent regular endoscopic variceal band ligation after primary bleeding episode every 2–4 weeks. Results Eradication of varices was achieved in 32% of cases in group A, and 46% in group was not any statistically significant (p > 0.05); also, there was no any statistical significant difference between both groups regarding occurrence of rebleeding, post endoscopic symptoms, and mortality rate (p > 0.05). Conclusion Band ligation and injection of esophageal and gastric vary every 2 months were as effective and safe as doing it every 2 to 4 weeks after primary bleeding episode for further studies and validation.

Antioxidants enhance the salt tolerance of date palms. A field experiment was carried out on date palm offshoots to improve the salt tolerance of the Sayer cultivar. Salicylic acid and citric acid (500 and 1,000 ppm) were used. The results showed that all growth parameters of plant height, leaf area, and leaf numbers decreased under the salinity conditions. The antioxidant applications increased the plant height, leaf area, carbohydrates, and relative water content compared with the control. Citric acid at 1,000 ppm decreased electrolyte leakage and malondialdehyde. Indoleacetic acid decreased, whereas abscisic acid increased under salinity. The antioxidant application increased indoleacetic, whereas abscisic acid decreased. Proline, protein content, and peroxidase activity increased under antioxidants. Also, the potassium and K/Na ratio increased under antioxidant applications. Citric acid improved the characteristics more than salicylic acid, encouraging farmers to use it for its low cost as an antioxidant to reduce environmental stress damage.

Infrastructure services are closely related to human life and development, as they are an essential indicator of the progress of life in countries. Infrastructure projects including the water sector are indeed the backbone of a country's economic development, providing many social, economic and environmental benefits. This paper aims to study the existing infrastructure (water sector) in Diyala Governorate, analyse the available data, prepare indicators of deficit ratios according to international standards, and propose possible solutions to address the problems in this sector. Data were collected for the water sector, which included the quantities of potable water supplied for the past eleven years. The results showed that the deficit rate in the water sector amounted to 32% in 2021, as evidenced by the deficit rates and the presence of clear deficiencies in the water sector in Diyala Governorate, and this deficiency does not result only from lack of availability, but rather due to a weak planning vision and the absence of future plans that take into account the increase in the governorate population. The results of predicting using simple linear regression using the Spss program version 26 showed that the demand for drinking water will reach 554,354 m³ per day in 2035. Three plans have been proposed to address the current deficit and future demand in the water sector, which is a short-term plan extending from 2022 to 2025, addressing 50% of the current deficit in 2021; A medium-term plan extending from 2022 to 2027 addresses the current deficit once and for all, and a long-term plan extending from 2022 to 2035 addresses the current deficit and future demand in the water sector.

A novel ligand, namely, (N’,N’’’-((1E,2E)-1,2-diphenylethane-1,2-diylidene)bis(3-allyl-2-hydroxybenzohydrazide) (H2DBAZ), was designed and synthesized. This ligand demonstrated the ability to successfully interact with Tb(III) ions, resulting in the formation of a chemosensor that exhibited luminescent properties. The novel ligand was produced and subsequently subjected to characterization with several analytical techniques, including mass spectroscopy, elemental analysis, Fourier-transform infrared spectroscopy (FTIR), and proton nuclear magnetic resonance spectroscopy (1H NMR). The postulated chemical structure of the Tb(III)–(DBAZ) complex was assessed utilizing a molar ratio approach. The chemosensor exhibited both selectivity and sensitivity towards malathion when compared to other nine organophosphorus pesticides that were investigated in methanol. The method was based on the phenomenon of luminescence static quenching shown by the complex subsequent to its interaction with the malathion pesticide. A linear Stern–Volmer plot was seen and, subsequently, utilized to generate the calibration curve. The observed linear range spanned from 0.39 to 60 µM, with a strong correlation coefficient of 0.999. Additionally, the limit of detection (LOD) was determined to be 0.118 µM. This methodology was successfully employed to measure the presence of malathion in various water samples. This particular complex exhibited promising potential for application in the development of a chemosensor utilizing the molecularly imprinted polymer approach.

Remote sensing (RS) data have allowed prospective zones of water accumulation (PZWA) that have been harvested during rainstorms to be revealed. Climatic, hydrologic, and geological data have been combined with radar and optical remote sensing data. A wide array of remote sensing data, including SRTM, Sentinel-1&2, Landsat-8, TRMM, and ALOS/PALSAR data, were processed to reveal the topographical characteristics of catchments (elevation, slope, curvature, and TRI) and geological (lineaments, lithology, and radar intensity), hydrological (Dd, TWI, and SPI), ecological (NDVI, InSAR CCD), and rainfall zones in Wadi Queih (WQ), which is an important drainage system that drains into the Red Sea. Radar data improved the structural elements and showed that the downstream area is shaped by the northeast–southwest (NE-SW) fault trend. After giving each evidential GIS layer a weight by utilizing a GIS-based, knowledge-driven methodology, the 13 GIS layers were integrated and combined. According to the findings, the studied basin can be classified into six zones based on how water resources are held and captured, which are very low, low, moderate, high, very high, and excellent. These zones correspond to 6.20, 14.01, 21.26, 36.57, 17.35, and 4.59% of the entire area. The results suggested a specific location for a lake that can be used to store rainwater, with a capacity of ~240 million m3 in the case of increasing rainfall yield. Such a lake complements the present lake at the end of WQ, which can hold about 1 million m3. InSAR coherence change detection (CCD) derived from Sentinel-1 data revealed noticeable changes in land use/land cover (LU/LC) areas. Areas that displayed changes in surface water signatures and agricultural and human activities were consistent with the predicted very high and excellent zones. Thus, the predicted model is an important approach that can aid planners and governments. Overall, the integration of optical and radar microwaves in RS and GIS techniques can reveal promising areas of rainwater and water accumulation.

Internet-based programs and communication techniques have become widely used and respected in the IT industry recently. A persistent source of "big data," or data that is enormous in volume, diverse in type, and has a complicated multidimensional structure, is internet applications and communications. Today, several measures are routinely performed with no assurance that any of them will be helpful in understanding the phenomenon of interest in an era of automatic, large-scale data collection. Online transactions that involve buying, selling, or even investing are all examples of e-commerce. As a result, they generate data that has a complex structure and a high dimension. The usual data storage techniques cannot handle those enormous volumes of data. There is a lot of work being done to find ways to minimize the dimensionality of big data in order to provide analytics reports that are even more accurate and data visualizations that are more interesting. As a result, the purpose of this survey study is to give an overview of big data analytics along with related problems and issues that go beyond technology.

Pure NiSe nanoparticles were successfully produced using an adapted thermal treatment technique and an alternate nitrogen flow. Throughout a range of calcination temperatures of 500 °C–800 °C, a number of techniques were utilised in order to examine the optical, structural and magnetic characteristics of the attained NiSe nanoparticles. Ultraviolet-visible absorption spectrophotometry was employed to ascertain the optical characteristics. These evidenced a reduction in the NiSe nanoparticle conduction band with elevated calcination temperatures, i.e. from 3.58 eV to 3.37 eV at 500 °C and 800 °C, respectively. This was attributed to a higher degree of attraction between the conduction electrons and the metallic ions with rising particle dimensions, equating to a larger atom population comprising the metal nanoparticles. This means that the findings can be applied to a wide range of energy applications. The lack of impurities within the produced NiSe nanoparticles was verified utilising Fourier-transform infrared spectroscopy and energy dispersive X-ray analysis. At calcination temperatures of ≥ 500 °C, powder X-ray diffraction demonstrated that the specimen, amorphous at room temperature, had undergone conversion into hexagonal crystalline nanostructures. Transmission electron microscopy confirmed the evolution of size NiSe nanoparticles; mean particle dimensions increased from 21 nm to 54 nm at calcination temperatures of 500 °C and 800 °C, respectively. Electron spin resonance spectroscopy, used to identify the magnetic properties, supported the presence of unpaired electrons.

Background:. We compared the surgical skills and outcomes of microsurgical fellows who completed an independent versus integrated plastic surgery residency. Methods:. We reviewed outcomes of abdominal wall reconstructions performed autonomously by microsurgical fellows at our institution from March 2005 to June 2019; outcome measures included hernia recurrence, surgical site occurrence, surgical site infection, length of hospital stay, unplanned return to the operating room, and 30-day readmission. The microsurgical skills were prospectively evaluated using the validated Structured Assessment of Microsurgical Skills at the start and end of the fellowship, in an animal laboratory model and clinical microsurgical cases. Multivariable hierarchical models were constructed to evaluate study outcomes. Results:. We identified 44 fellows and 118 consecutive patients (52% women) who met our inclusion criteria. Independent fellows performed 55% (n = 65) of cases, and 45% were performed by integrated fellows. We found no significant difference in hernia recurrence, surgical site occurrences, surgical site infections, 30-day readmission, unplanned return to the operating room, or length of stay between the two groups in adjusted models. Although laboratory scores were similar between the groups, integrated fellows demonstrated higher initial clinical scores (42.0 ± 4.9 versus 37.7 ± 5.0, P = 0.04); however, the final clinical scores were similar (50.8 ± 6.0 versus 48.9 ± 5.2, P = 0.45). Conclusions:. Independent and integrated fellows demonstrated similar long-term patient outcomes. Although integrated fellows had better initial microsurgical skills, evaluation at the conclusion of fellowship revealed similar performance, indicating that fellowship training allows for further development of competent surgeons.

Due to the increasing number of people driving cars, the number of insurance claims has also increased. This process involves the manual assessment of the vehicle by an insurance company's service engineer, as well as the physical inspection by a licensed insurance company representative. An end-to-end solution has been proposed that would allow the customer and the insurance company to automate the process of recognizing the damaged area in the vehicles and estimating the cost of the damage. It would allow them to get a better understanding of the condition of the vehicle. For this purpose, A deep learning, Mask Region-based Convolutional Neural Network (Mask RCNN) model was utilized in this work to classify vehicle damages costs. Two Mask RCNN models were utilized, the first one was used to detect the sides of the vehicle, which will affect damage cost estimation and the second was used to find the area of the damage. The Experimental work shows that the proposed model gives reasonable results to estimate the cost of the damage. We achieve an accuracy of 98.5% with the combination of the two Mask RCNN models. And showed that Mask RCNN has a promising result to detect the area of the damage in the car.

One of the most important problems of Iraqi construction projects is the cost variances, so it is important to identify the problems and shortcomings that cause poor cost control. Through the utilization of questionnaires, the study evaluated how project costs were managed and reported. The questionnaire was distributed to 180 professionals working in the Iraqi construction sector, with a response rate of 91%. The results showed that a high percentage of projects are implemented with a difference between real and estimated costs, and the process of documenting cost data needs to be more secure. On the other hand, there is a weakness in providing the necessary work structure information to monitor costs and a lack of processing of the required data regarding mechanisms and equipment and problems. It is related to the accuracy of the estimation and the management and documentation of labor wages. Most of the problems are the lack of appropriate systems to implement cost control appropriately.

The process of risk assessment in the build-operate transfer (BOT) project is very important to identify and analyze the risks in order to make the appropriate decision to respond to them. In this paper, AHP Technique was used to make the appropriate decision regarding response to the most prominent risks that were generated in BOT projects, which includes a comparison between the criteria for each risk as well as the available alternatives and by mathematical methods using matrices to reach an appropriate decision to respond to each risk.Ten common risks in BOT contracts are adopted for analysis in this paper, which is grouped into six main risk headings.The procedures followed in this paper are the questionnaire method to assign the weights of each criterion and each alternative based on the evaluations of 10 experts in BOT contracts after building the questionnaire model.The appropriate decision was reached to respond to each risk, and then the best measures taken were made based on literature reviews of previous research regarding the procedures for risk response plans.

Injuries sustained by sugarbeet (Beta vulgaris L.) roots during harvest and postharvest operations seriously reduce the yield of white sugar produced from stored roots. Although wound healing is critically important to reduce losses, knowledge of these processes is limited for this crop as well as for roots in other species. To better understand the metabolic signals and changes that occur in wounded roots, dynamic changes in gene expression were determined by RNA sequencing and the activity of products from key genes identified in this analysis were determined in the 0.25 to 24 h following injury. Nearly five thousand differentially expressed genes that contribute to a wide range of cellular and molecular functions were identified in wounded roots. Highly upregulated genes included transcription factor genes, as well as genes involved in ethylene and jasmonic acid (JA) biosynthesis and signaling and phenolic compound biosynthesis and polymerization. Enzyme activities for key genes in ethylene and phenolic compound biosynthesis and polymerization also increased due to wounding. Results indicate that wounding causes a major reallocation of metabolism in sugarbeet taproots. Although both ethylene and JA are likely involved in triggering wound responses, the greater and more sustained upregulation of ethylene biosynthesis and signaling genes relative to those of JA, suggest a preeminence of ethylene signaling in wounded sugarbeet roots. Changes in gene expression and enzymes involved in phenolic compound metabolism additionally indicate that barriers synthesized to seal off wounds, such as suberin or lignin, are initiated within the first 24 h after injury.

Mohammed S Alqahtani,1,2 Mohamed Abbas,3,4 Abdullah Alsabaani,5 Amjad Alqarni,1 Hussain M Almohiy,1 Entisar Alsawqaee,1 Raghad Alshahrani,1 Shahd Alshahrani1 1Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha, 61421, Saudi Arabia; 2BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE1 7RH, U.K; 3Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia; 4Computers and Communications Department, College of Engineering, Delta University for Science and Technology, Gamasa, 35712, Egypt; 5Department of Family and Community Medicine, College of Medicine, King Khalid University, Abha, 61421, Saudi ArabiaCorrespondence: Mohamed Abbas, Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia, Tel +966 17 241 1866, Fax +966 17 242 8184, Email mabas@kku.edu.saAbstract: Heart attacks, arrhythmias, and cardiomyopathy are all linked to the 2019 coronavirus disease (COVID-19), which has been identified as a risk factor for cardiovascular disease. Nothing can be held accountable in the current state of affairs. Undiagnosed chronic systolic heart failure (CSHF) develops when the heart’s second half of the cardiac cycle does not function properly. As a result, the heart’s blood pumping function is interrupted. Stress-induced cardiomyopathy may be caused by a variety of factors inside the body (SICM). Cytokine storm and microvascular dysfunction are among the issues. There is inflammation in the heart muscle, which may lead to stress-induced cardiomyopathy. A major part of our study is going to be devoted to understanding the effects of coronavirus on the cardiovascular system and blood vessels. A lot of time and effort has been put into figuring out the health effects of radiation exposure. The heart and circulatory system are shown to be affected by the coronavirus in this research. COVID-19 is shown to influence persons with heart disease, heart failure, arrhythmias, microvascular angiopathy, and cardiac damage in this study.Keywords: coronavirus, heart failure, cardiac diseases, myocardial injury, arrhythmias

Metaverse is a vision enabling to constitute an environment in which someone could see real and virtualized worlds. The Metaverse is a product (or something similar as we do not yet know its final form) of such technologies. In this circumstance, when applications that utilize the Metaverse are used, there seem to be no transportation charges, and there is no cap on amounts of individuals, users, players, learners, or trainee who can take part. Hence, and due to such a feature, the Metaverse has attracted various researchers from different fields where it has been exploited by them to contribute to those fields and research areas. As for example, it is possible to teach various target audiences by offering different events and classes from any location in the globe. In order for a participant to utilize the Metaverse, there are necessary conditions to be considered as well as other settings to be initialized. In line of this, virtual reality, augmented reality, availability of required sensors, smart glasses, headsets, and few others are considered some examples of such conditions and settings that the Metaverse requires. Despite the advantages that Metaverse offers us, there are a number of considerations that must be taken into account while developing it by interested researchers. One of these concerns is the Metaverse privacy regarding the participants (represented as avatars inside the Metaverse environment). Another issue is that since the Metaverse is still in its early stages, many attempts have to be made from interested researchers who engage to develop it to enhancing it. This review aims to survey related articles that concern the Metaverse and its development providing a review of the chronological stages throughout the history of the development of Metaverse. It aims also to list a number of recent technological advances allowing the Metaverse. Besides, Metaverse’s definitions, properties, architecture, and applications have been discussed and listed in this review. The novelty of this article is that it has suggested a framework to a number of issues that are still paid attention for potential solutions by researchers aiming to contribute to researchers and designers to consider such an issue and its corresponding solution for future research works and enhancement. Challenges faced by researchers and other relevant concerned issues related to Metaverse have been in detail discussed and highlighted. Besides, future trends have been clarified.